TL;DR
SH-m5U was evaluated using Apache Spark and Apache Hadoop on datasets of up to 10 million RNA sequences, where Spark achieved comparable classification accuracy with an average computational speedup of 4.80× over Hadoop.
By integrating structure selection with binding-preference inference, PRIS provides an efficient framework for large-scale RNA library screening and aptamer design.
Identifying transcription factor binding sites (TFBSs) is fundamental to understanding complex gene regulatory mechanisms and the functions of non-coding regions. Although existing methods have achieved substantial strides, capturing both local structural features and long-range spatial dependencies within DNA sequence...
HyLnc demonstrates that integrating deep contextual representations with biologically interpretable features enhances lncRNA prediction, and provides a robust and scalable solution for large-scale transcriptome annotation and can be extended to other sequence-based prediction.
UFold-X, a dual-branch deep learning framework that combines a convolutional encoder for local structure modeling with a Mamba-based Visual State Space Module for capturing long-range dependencies, is presented, achieving the best performance among deep learning-based methods.
RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains l...
A multimodal deep learning framework that integrates molecular surface fingerprints, contextual sequence embeddings from a pretrained RNA language model and dot bracket secondary structure encoding to predict ligand binding sites on RNA, which provides an accurate and generalizable tool for nucleotide resolution bindin...
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